Hybrid Machine Learning Models for Distributed Biological Data in Multi-Cloud Environment

In real-world environment, the term big data is referred to portray the huge volume of complex structured and unstructured data, which is growing exponentially very fast in time. It is mainly applicable for overgrowth of biological data which is processed by cloud source. Cloud computing refers to processing and storing the massive volume of data over the Internet instead of single computer’s hard drive. Various types of services offer to process the data. Cloud provides most of the intelligent services like security, performance, productivity, reliability, scalability, speed, and accurate access. The data is distributed among various places and various sizes. Centralize all the data into single site is to increase the processing speed and memory. A distributed approach employed in the present study is to replace the centralized data environment. The distributed refers to the collection of independent components. To access the data by distributed way, that is, processing the data based on feature selection in data source and getting the data representative based on this, the informative data will be collected into single site. The hybrid machine learning and deep learning models are used to detect the diseases in biological data to improve the computational efficiency and reduce the memory. The hybrid distributed models show the excellent performance in biological research.